evidence {AICcmodavg}R Documentation

Compute Evidence Ratio Between Two Models

Description

This function compares two models of a candidate model set based on their evidence ratio (i.e., ratio of Akaike weights). The default computes the evidence ratio of the Akaike weights between the top-ranked model and a lower-ranked model. You must supply a model selection table of class 'aictab' as the first argument.

Usage

evidence(aic.table, model.high = "top", model.low)

Arguments

aic.table a model selection table of class 'aictab' such as that produced by 'aictab'.
model.high the top-ranked model (default), or alternatively, the name of another model as it appears in the model selection table.
model.low the name of a lower-ranked model such as it appears in the model selection table.

Details

The default compares the Akaike weights of the top-ranked model to another model of the candidate model set. The evidence ratio can be interpreted as the number of times a given model is more parsimonious than a lower-ranked model. If one desires an evidence ratio that does not involve a comparison with the top-ranking model, the name of the required model must be specified in the model.high argument.

Value

'evidence' produces an object of class 'evidence' with the following components:

Model.high the top-ranked model among the two compared.
Model.low the lower-ranked model among the two compared.
Ev.ratio the evidence ratio between the two models compared.

Author(s)

Marc J. Mazerolle

References

Burnham, K. P., Anderson, D. R. (2002) Model Selection and Multimodel Inference: a practical information-theoretic approach. Second edition. Springer: New York.

See Also

AICc, aictab, c_hat, modavg, importance, confset, modavgpred

Examples

##run example from Burnham and Anderson (2002, p. 183) with two
##non-nested models
data(pine)
Cand.set <- list( )
Cand.set[[1]] <- lm(y ~ x, data = pine)
Cand.set[[2]] <- lm(y ~ z, data = pine)

##assign model names
Modnames <- c("raw density", "density corrected for resin content")

##compute model selection table
aicctable.out <- aictab(cand.set = Cand.set, modnames = Modnames)

##compute evidence ratio
evidence(aic.table = aicctable.out, model.low = "raw density")           
##round to 4 digits after decimal point
print(evidence(aic.table = aicctable.out, model.low = "raw density"),
digits = 4)

##run models for the Orthodont data set in nlme
require(nlme)

##set up candidate model list
Cand.models <- list()
Cand.models[[1]] <- lme(distance ~ age, data = Orthodont, method = "ML")
##random is ~ age | Subject
Cand.models[[2]] <- lme(distance ~ age + Sex, data = Orthodont, random =
~ 1, method = "ML")
Cand.models[[3]] <- lme(distance ~ 1, data = Orthodont, random = ~ 1,
method = "ML")

##create a vector of model names
Modnames <- NULL
for (i in 1:length(Cand.models)) {
Modnames[i] <- paste("mod", i, sep = "")
}

##compute AICc table
aic.table.1 <- aictab(cand.set = Cand.models, modnames = Modnames,
second.ord = TRUE)

##compute evidence ratio between best model and second-ranked model
evidence(aic.table = aic.table.1, model.high = "top", model.low =
"mod1")  

##compute evidence ratio between second-best model and third-ranked model 
evidence(aic.table = aic.table.1, model.high = "mod1", model.low =
"mod3")

[Package AICcmodavg version 1.05 Index]